Data Architecture Design: Designing and implementing data architectures that support data integration, transformation, and storage, ensuring scalability and high performance
Data Pipeline Development: Building and maintaining data pipelines to extract, transform, and load (ETL) data from various sources into data warehouses or data lakes
Big Data Technologies: Working with big data technologies like Hadoop, Spark, and Kafka to process and manage large volumes of data efficiently
Data Modeling: Designing data models and schemas to support business intelligence, analytics, and reporting requirements
Data Quality and Governance: Implementing data quality checks and data governance processes to ensure data accuracy, consistency, and compliance with data policies
Performance Optimization: Identifying performance bottlenecks in data processing workflows and implementing optimizations to improve data pipeline efficiency
Data Security: Implementing data security measures to protect sensitive data and ensure compliance with data privacy regulations
